CN103888204B - The modeling method of the multiple dimensioned fading model of maize field radio sensor network channel - Google Patents

Abstract

The present invention relates to the communications field, the modeling method of the multiple dimensioned fading model of a kind of maize field radio sensor network channel is disclosed, specifically comprise: S1., according to the envirment factor affecting radio signal propagation in sample area, extracts the key factor causing channel space and time difference; S2., under the varying environment factor and key factor condition, the radio signal propagation characteristic in sample area is gathered; S3. according to the data gathered and described key factor, the multiple dimensioned fading model modeling of channel is carried out.The present invention can realize the formulistic modeling to wireless sensor network signal transmission in maize field gradient environment, for the deployment of radio sensor network monitoring application interior joint, topology control, Route Selection etc. provide channel theory to support.

Description

The modeling method of the multiple dimensioned fading model of maize field radio sensor network channel

Technical field

The present invention relates to the communications field, be specifically related to the modeling method of the multiple dimensioned fading model of maize field radio sensor network channel.

Background technology

Agricultural land for growing field crops mainly refers to that those carry out the soil of large area crop-planting, and the crop being generally used for field planting has: wheat, corn, potato etc.The important step of high-quality plant development has been become based on the collection of the agriculture field planting environmental information of wireless sensor network, transmission, monitoring.Radio communication is the basis of wireless sensor network data transmission, wireless signal is in communication process, can be there is reflection in various degree, the phenomenon such as scattering and diffraction in the impact by variety classes barrier, cause the decline of signal energy, and the change of time delay, phase place, frequency etc.In farmland, crop all experiences Different growth phases every year, and the branches and leaves density variation in these stages is very large, signal is propagated to the impact produced in various degree.

In existing radio network information channel decline Modeling Research, some research emphasis are the correlations between the decline of signal large scale and environmental factor, some research needs distinguishable multiple paths, the modeling of signal large scale is only studied in some research, and some research only carries out the modeling of signal small scale from desirable statistical model.

The shortcoming of existing scheme is:

1, research emphasis is that the scheme of correlation between signal strength signal intensity and environmental factor does not consider the impact of multipath fading on signal strength signal intensity and signal quality, not accurate enough to choosing of environment characteristic parameters;

2, research needs the scheme needs differentiating multiple paths effectively to distinguish each reflection, scattering component, and Reconstruction is carried out to each paths, but in actual applications, especially in farmland production environment, because shelter is intensive, channel condition is complicated, without obvious distinguishable multiple paths, also just cannot be described reconstruction with several limited reflections, scattering path to channel;

Further investigation discussion is not carried out in the impact of the complicated gradient environment factor pair channel propagation model in farmland by the scheme 3, only studying the modeling of signal large scale.Though the method can describe channel circumstance in large scale, because not associating with site environment parameter, also just cannot carry out forecast analysis according to measurable environmental parameter to the fading factor in similar scene, practical value is low;

4, only the scheme of signal small scale modeling is carried out by setting up the scattering object geometry distribution in wireless channel from desirable statistical model, the methods such as ray trace are adopted to study modeling, but the gradual change of corn growth circumstance complication, without obvious distinguishable multipath, be difficult to adopt ray tracing method to carry out small scale modeling.

Summary of the invention

Technical problem to be solved by this invention is that prior art is not considered in corn planting complex environment, multiple circulation way exists and without obvious distinguishable multi-path influence simultaneously, and and not exclusively separate two kinds of yardsticks decline on the various factors under the impact of signal propagation characteristics and corn planting complex environment, cause radio network information channel to decline modeling accuracy is low even can not modeling.

For this purpose, the present invention proposes the modeling method of the multiple dimensioned fading model of maize field radio sensor network channel, and the method comprises:

S1. according to the envirment factor affecting radio signal propagation in sample area, the key factor causing channel space and time difference is extracted;

S2., under the varying environment factor and key factor condition, the radio signal propagation characteristic in sample area is gathered;

S3. according to the data gathered and described key factor, the multiple dimensioned fading model modeling of channel is carried out.

Wherein, in step sl, described envirment factor comprises the height H of crop in sample area _p, crop leaf area A _l, crop stem stalk area A _c, crop fruit surface area A _f, land area A in sample area _g, distance d between transmitting antenna and reception antenna; What described key factor comprised crop blocks height H _b, crop surface area density indices P SAD, described H _b=H _a-H _p, wherein H _afor antenna height, the computing formula of described PSAD is as follows:

$\mathrm{PSAD}=\frac{A_l+A_c+A_f}{A_G\×H_p}.$

Wherein, in step s 2, described radio signal propagation feature comprises: signal strength signal intensity, Packet Error Ratio.

Wherein, in step s 2, described sample area comprises H _bthe sample area of > 0 and H _bthe sample area of < 0, described radio signal propagation characteristic comprises H _bthe radio signal propagation characteristic of the sample area of > 0 and H _bthe radio signal propagation characteristic of the sample area of < 0.

Wherein, between step S2 and step S3, the method comprises further:

S21. at described H _bthe sample area of > 0, carries out channel large scale decline PL _bcalculating;

S22. described H is obtained _bthe radio signal propagation characteristic of the sample area of > 0, carries out matching to the data gathered, obtains channel large scale fading model.

Wherein, in the step s 21, described channel large scale decline PL _bfor:

${\mathrm{PL}}_b=10.1g\left(\frac{P_s}{P_r}\right)$

Wherein, P _sfor the transmitting power of transmitting antenna, P _rfor the power module of the wireless signal that reception antenna receives;

Described $P_r=\frac{P_s{G}_s{G}_r{\mathrm{\&lambda;}}^2}{{\left(4\mathrm{\&pi;}\right)}^2{d}^n};$

Wherein, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, λ is wavelength, and d is the distance between transmitting antenna and reception antenna, and n is fading factor.

Wherein, described step S22 comprises: obtain described H _bthe radio signal propagation characteristic of the sample area of > 0, by described radio signal propagation data to described P _rcarry out matching, obtain fading factor n and H _b, PSAD functional relation:

n=a·ln(H _b)+b·ln(PSAD)+c

Described channel large scale fading model is:

PL=(a·ln(H _b)+b·ln(PSAD)+c)lgd+A

Wherein, H _b> 0, PL is the decline of the wireless signal that transmitting antenna sends, and a, b and c are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines, H _bfor blocking height;

Wherein, f is radio signal frequency, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, c is speed of light constant.

Wherein, described step S3 comprises:

The envelope gross power P of the wireless signal S31. received according to reception antenna, described envelope gross power P comprises large scale component power P _band small scale multipath noise power P _m, build described P _mmodel:

$P_m=\frac{P-P_b}{2}$

Wherein, the formula of described P is as follows:

$P=P_s\&CenterDot;\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l_i}^2$

Wherein, P _sfor the transmitting power of transmitting antenna, N is the number of multiple paths; l _iit is the reflection fading coefficients of the i-th paths;

S32. according to described P _m, build signal to noise ratio snr model;

$\mathrm{SNR}=\frac{P_b}{P_m+\mathrm{AWGN}};$

Wherein, AWGN is additive white Gaussian noise;

S33. according to described SNR, Packet Error Ratio PER model is built;

$\mathrm{PER}=\frac{(M-1)}{2}\&times;e^{-\frac{\mathrm{SNR}}{2}};$

Wherein, M is the heterogeneous coefficient of modulation;

S34. described H is obtained _bthe radio signal propagation characteristic of the sample area of < 0, obtains the Packet Error Ratio PER measured value of reception antenna;

According to H _benvironment key factor H in the sample area of < 0 _b, PSAD, and channel large scale fading model, obtains described large scale component power P _bpredicted value P _b';

S35. according to described PER measured value and described large scale component power P _bpredicted value P _b', to described PER model and P _mmodel carries out curve fitting, and obtains the multiple dimensioned fading model of channel:

$\mathrm{PL}=(a^{\&prime;}\&CenterDot;\ln \left(H_b\right)+b^{\&prime;}\&CenterDot;\ln \left(\mathrm{PSAD}\right)+c^{\&prime;})1\mathrm{gd}+1g(i\&CenterDot;e^{H_b}+j\&CenterDot;e^{\mathrm{PSAD}}+k)+B$

Wherein, H _b<0, PL are the decline of the wireless signal that transmitting antenna sends, and a ', b ', c ', i, j, k are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines;

Wherein, $B=201\mathrm{gf}-101g\left[\frac{G_s{G}_r{c}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\right]+1g\frac{2}{3},$ F is radio signal frequency, G _sfor transmitter antenna gain (dBi), c is speed of light constant.

Preferably, described method comprises further: S4. is according to coefficient R ²evaluate the model of described acquisition, wherein said R ²computing formula as follows:

$R^2=1-\frac{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\widehat{\mathrm{\&xi;}]}}^2}{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\stackrel{\&OverBar;}{\mathrm{\&xi;}}]}^2}$

Wherein, ξ _ithe radio signal propagation characteristic gathered, described ξ _iregressand value, for described ξ _imean value, S is the number of collecting sample point.

Compared to prior art, the beneficial effect of method provided by the invention is: according in maize field complex environment factor impact as, spacing in the rows, corn growth situation parameter are as the dense degree etc. of plant height, blade and fruit, and the antenna height of monitoring node, relative distance etc., extract space and time difference key factor, modeling is carried out to the wireless channel decline under maize field environment.Consider the large scale of wireless signal under the complex environment of farmland and multipath fading effect, selective analysis cause due to crop dense growth without the probability distribution of the Small-scale fading such as distinguishable multipath to communication signal quality, carry out power spectral density to solve, the parameterized model of channel fading characteristic is drawn, in the application of corn planting environment radio sensor network monitoring, subsequent communications prediction of quality, node location deployment, network topology control, coverage metric, routing optimization etc. provide basic theory basis and foundation eventually through measured data matching.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.

Fig. 1 shows the modeling method flow chart of the multiple dimensioned fading model of maize field radio sensor network channel;

Fig. 2 shows the modeling method flow chart of the multiple dimensioned fading model of maize field radio sensor network channel of embodiment 2;

Fig. 3 shows H _bthe sample area of > 0;

Fig. 4 shows H _bthe sample area of < 0;

Fig. 5 shows the variation relation figure of signal strength signal intensity under space and time difference condition and envirment factor;

Fig. 6 shows Packet Error Ratio and communication distance, blocks graph of a relation highly.

Embodiment

For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

Embodiment 1:

The modeling method of the open multiple dimensioned fading model of maize field radio sensor network channel of the present embodiment, as shown in Figure 1, the method comprises:

The present invention proposes the modeling method of the multiple dimensioned fading model of maize field radio sensor network channel, and the method comprises:

S1. according to the envirment factor affecting radio signal propagation in sample area, the key factor causing channel space and time difference is extracted;

S2., under the varying environment factor and key factor condition, the radio signal propagation characteristic in sample area is gathered;

S3. according to the data gathered and described key factor, the multiple dimensioned fading model modeling of channel is carried out.

Wherein, in step sl, described envirment factor comprises the height H of crop in sample area _p, crop leaf area A _l, crop stem stalk area A _c, crop fruit surface area A _f, land area A in sample area _g, distance d between transmitting antenna and reception antenna; What described key factor comprised crop blocks height H _b, crop surface area density indices P SAD, described H _b=H _a-H _p, wherein H _afor antenna height, the computing formula of described PSAD is as follows:

$\mathrm{PSAD}=\frac{A_l+A_c+A_f}{A_G\&times;H_p}.$

Wherein, in step s 2, described radio signal propagation feature comprises: signal strength signal intensity, Packet Error Ratio.

Wherein, in step s 2, described sample area comprises H _bthe sample area of > 0 and H _bthe sample area of < 0, described radio signal propagation characteristic comprises H _bthe radio signal propagation characteristic of the sample area of > 0 and H _bthe radio signal propagation characteristic of the sample area of < 0.

Wherein, between step S2 and step S3, the method comprises further:

S21. at described H _bthe sample area of > 0, carries out channel large scale decline PL _bcalculating;

S22. described H is obtained _bthe radio signal propagation characteristic of the sample area of > 0, carries out matching to the data gathered, obtains channel large scale fading model.

Wherein, in the step s 21, described channel large scale decline PL _bfor:

${\mathrm{PL}}_b=10.1g\left(\frac{P_s}{P_r}\right)$

Wherein, P _sfor the transmitting power of transmitting antenna, P _rfor the power module of the wireless signal that reception antenna receives;

Described $P_r=\frac{P_s{G}_s{G}_r{\mathrm{\&lambda;}}^2}{{\left(4\mathrm{\&pi;}\right)}^2{d}^n};$

Wherein, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, λ is wavelength, and d is the distance between transmitting antenna and reception antenna, and n is fading factor.

Wherein, described step S22 comprises: obtain described H _bthe radio signal propagation characteristic of the sample area of > 0, by described radio signal propagation data to described P _rcarry out matching, obtain fading factor n and H _b, PSAD functional relation:

n=a·ln(H _b)+b·ln(PSAD)+c

Described channel large scale fading model is:

PL=(a·ln(H _b)+b·ln(PSAD)+c)lgd+A

Wherein, H _b> 0, PL is the decline of the wireless signal that transmitting antenna sends, and a, b and c are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines, H _bfor blocking height; At H _bduring > 0, the Small-scale fading of channel is ignored, and only considers large scale effect, so there is PL _b=PL;

Wherein, f is radio signal frequency, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, c is speed of light constant.

Wherein, described step S3 comprises:

The envelope gross power P of the wireless signal S31. received according to reception antenna, described envelope gross power P comprises large scale component power P _band small scale multipath noise power P _m, build described P _mmodel:

$P_m=\frac{P-P_b}{2}$

Wherein, the formula of described P is as follows:

$P=P_s\&CenterDot;\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l_i}^2$

Wherein, P _sfor the transmitting power of transmitting antenna, N is the number of multiple paths; l _iit is the reflection fading coefficients of the i-th paths;

S32. according to described P _m, build signal to noise ratio snr model;

$\mathrm{SNR}=\frac{P_b}{P_m+\mathrm{AWGN}};$

Wherein, AWGN is additive white Gaussian noise;

S33. according to described SNR, Packet Error Ratio PER model is built;

$\mathrm{PER}=\frac{(M-1)}{2}\&times;e^{-\frac{\mathrm{SNR}}{2}};$

Wherein, M is the heterogeneous coefficient of modulation;

S34. described H is obtained _bthe radio signal propagation characteristic of the sample area of < 0, obtains the Packet Error Ratio PER measured value of reception antenna;

According to H _benvironment key factor H in the sample area of < 0 _b, PSAD, and channel large scale fading model, obtains described large scale component power P _bpredicted value P _b;

S35. according to described PER measured value and described large scale component power P _bpredicted value P _b, to described PER model and P _mmodel carries out curve fitting, and obtains the multiple dimensioned fading model of channel:

$\mathrm{PL}=(a^{\&prime;}\&CenterDot;\ln \left(H_b\right)+b^{\&prime;}\&CenterDot;\ln \left(\mathrm{PSAD}\right)+c^{\&prime;})1\mathrm{gd}+1g(i\&CenterDot;e^{H_b}+j\&CenterDot;e^{\mathrm{PSAD}}+k)+B$

Wherein, H _b<0, PL are the decline of the wireless signal that transmitting antenna sends, and a ', b ', c ', i, j, k are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines;

Wherein, $B=201\mathrm{gf}-101g\left[\frac{G_s{G}_r{c}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\right]+1g\frac{2}{3},$ F is radio signal frequency, G _sfor transmitter antenna gain (dBi), c is speed of light constant.

Preferably, described method comprises further: S4. is according to coefficient R ²evaluate the model of described acquisition, wherein said R ²computing formula as follows:

$R^2=1-\frac{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\widehat{\mathrm{\&xi;}]}}^2}{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\stackrel{\&OverBar;}{\mathrm{\&xi;}}]}^2}$

Wherein, ξ _ithe radio signal propagation characteristic gathered, described ξ _iregressand value, for described ξ _imean value, S is the number of collecting sample point.

Embodiment 2:

The modeling method of the open multiple dimensioned fading model of maize field radio sensor network channel of the present embodiment, affects three kinds of basic transmission meanss of radio propagation for reflection, diffraction and scattering in the present embodiment.From farm environment self-organizing network signal transmission path, the electromagnetic wave sent from transmitting node is propagated to receiving node with three kinds of different modes primarily of three paths:

Straightline propagation, blocking if any crop, electromagnetic wave is then propagated in the mode of scattering;

Part electromagnetic wave received node after ground return receives;

Part electromagnetic wave to crop top emission, and crop top end produce diffraction, after received by receiving node.

It should be noted that three kinds of modes exist simultaneously, and also not exclusively separate, so when considering Channel Modeling, should consider comprehensively.

The modeling method of the multiple dimensioned fading model of maize field radio sensor network channel disclosed in the present embodiment, as shown in Figure 2, specifically comprises:

1. space and time difference key factor extracts

The envirment factor affecting radio signal propagation is numerous, how from various factors, to extract the key factor causing channel circumstance space and time difference, one of emphasis becoming modeling.

First, whether environment is propagated signal and is caused that to block be the matter of utmost importance of Channel Modeling, simple according to antenna height, directly cannot judge that whether environment causes signal los path and block, to block height H in the present embodiment _bfor parameter carries out Channel Modeling, wherein

H _b=H _a-H _p

Wherein H _afor antenna height, H _pfor plant height.

If H _bbe greater than 0, as shown in Figure 3, then illustrating to there is unobstructed single order Ferned Area between communication node, is line-of-sight transmission, main consideration large scale fading effect; If H _bbe less than 0, as shown in Figure 4, then between communication node, be obstructed in single order Ferned Area, must consider large scale decline and multipath fading simultaneously.

The coverage extent that its secondary environment is propagated signal is obviously relevant to the dense degree of crop, and corresponding reflection and scattering process after electromagnetic wave incident to crop surface, can be produced, the present invention introduces crop surface and amasss dnesity index PSAD(Plant Surface Area Density thus) in order to characterize plant growth dense degree, be defined as unit group falling bodies and amass interior crop total surface area, with m ²/ m ³represent, computing formula is as follows:

$\mathrm{PSAD}=\frac{A_l+A_c+A_f}{A_G\&times;H_p}$

Wherein A _lfor Crop leaf area in sample area, A _cfor crop stalk area in sample area, A _ffor crop and fruit surface area in sample area, A _gfor land area in sample area, H _pfor plant height.At H _bwhen being less than 0, when namely signal line-of-sight propagation is obstructed, the PSAD Parameter fusion parameters such as leaf area, fruit stem area, plant height, spacing in the rows, better can embody the dense degree of plant growth.Further, because milpa the middle and late growth stage up and down and uneven, so not identical in the value of differing heights PSAD, generally can be divided into bottom, leaf layer, canopy three part to carry out measuring and analysis to PSAD.

The propagation model of wireless channel can be divided into large scale propagation model and small scale propagation model two kinds.Large-scale model is mainly used in describing the change in signal strength between transmitter and receiver on long distance (hundreds of or a few km), in general large scale decline and the distance between transmitting antenna and reception antenna are inversely proportional to, and have different fading factors in different areas (as seashore and hinterland, city and rural area).Small-scale model is for describing the Rapid Variable Design of short distance (several wavelength) or short time (level second) interior received signal strength, but these two kinds of models are not separate, in same wireless channel, both there is large scale decline, also there is multipath fading.

2. large scale decline modeling

Work as H _bwhen being greater than 0, between launch and accept node, single order Ferned Area is unobstructed, mainly considers large scale effect during channel fading modeling.Large scale fading model is comparatively fixing, and basic model is index fading model.The power of the wireless signal received for reception antenna is

$P_r=\frac{P_s{G}_s{G}_r{\mathrm{\&lambda;}}^2}{{\left(4\mathrm{\&pi;}\right)}^2{d}^n}$

In formula, P _sfor the transmitting power of transmitting node; G _s, G _rbe respectively transmitting antenna and receiving antenna gain; λ is wavelength; D is the distance between transmitting antenna and reception antenna; N is the fading factor with environmental correclation, n=2 time in free space, n>2 under all the other conditions.

Under large scale fade condition, with the channel fading PL in logarithmic form definition signal transmitting procedure _bhave:

${\mathrm{PL}}_b=10\&CenterDot;\lg \left(\frac{P_s}{P_r}\right)=10\&CenterDot;\mathrm{nlgd}+20\mathrm{lgf}-10\lg \left[\frac{G_s{G}_r{c}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\right]$

At H _bduring > 0, the Small-scale fading of channel is ignored, and only considers large scale effect, so there is PL _b=PL, PL are the decline of the wireless signal that transmitting antenna sends;

For extensive farm environment self-organizing application network, f, Gs, Gr are determined value, and add that c and π is constant, variable only has distance d, and with the fading factor n of environmental correclation.For the key of large scale decline modeling, be that carrying out formulism to environment fading factor n describes.

Measured data is propagated, the signal strength signal intensity under acquisition space and time difference condition and the variation relation of envirment factor, as shown in Figure 5 according to the signal in corn planting environment.To H _bbe greater than 0 partial data and carry out matching, the approximating methods such as available least square method carry out Multiple Factor Fitting to environment fading factor, draw fading factor n and block height H _band crop surface amasss the functional relation between dnesity index PSAD.

PL=(a·ln(H _b)+b·ln(PSAD)+c)lgd+A (H _b>0)

Wherein a, b, c are fitting coefficient, and constantly change along with plant growth, in Different growth phases, the value of PSAD is different, thus can draw different fitting coefficients, general to corn growth process, can divide emerge, jointing, heading three phases carry out modeling analysis, wherein

$A=20\mathrm{lgf}-10\lg \left[\frac{G_s{G}_r{c}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\right]$

It is a constant under established condition.Distinguishingly, the ln that in formula, matching uses, the functions such as lg are not unique solution, according to embodiment of the present invention measured data, use the fitting degree of this function higher, and have certain representativeness.

3. multiple dimensioned associating modeling

Work as H _bwhen being less than or equal to 0, the Small-scale fading that signal is propagated is remarkable gradually, and most important two key elements affecting multipath fading are exactly multipath effect and Doppler effect.Under agricultural planting condition, the position that monitoring node is does not change in time, is static network, so without the need to considering Doppler effect.Along with environment propagates increasing the weight of of coverage extent to signal, the line-of-sight propagation path of signal is blocked, and crop surface can only be passed through, the reflections such as ground, scattering, or the mode such as the diffraction of canopy is propagated, thus the multiple different transmission path formed, it causes each path arriving signal to have different amplitudes, phase place and time delay, therefore time dispersive effect and the frequency selective fading of signal can be produced, below are all factors that analyses of Multipath Effects modeling needs to consider, but due to corn planting circumstance complication, signal is by forming countless differentiated transmission path after intensive plant, existing method cannot carry out effective modeling analysis, the invention provides multipath fading modeling method under a kind of power spectrumanalysis corn environment.

The complex envelope of transmit band messenger is:

$\tilde{s}\left(t\right)=\mathrm{Re}\left[s\left(t\right)e^{j2\mathrm{\&pi;}{f}_{c}t}\right]$

Wherein, f _cfor signal carrier frequency, R _erepresent the real part of complex signal, if co-exist in N bar multipath propagation paths, make the _ithe path of paths is d _i, reflection fading coefficients is l _i, the light velocity is c, and the equal position of all nodes is fixed, and is static network, there is not Doppler effect, then Received signal strength is each paths signal sum,

$\tilde{r}\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l}_i\tilde{s}(t-\frac{d_i}{c})$

Signal will be sent substitute into, obtain

$\tilde{r}\left(t\right)=\mathrm{Re}\left[\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l}_i{e}^{j2\mathrm{\&pi;}{f}_c(t-{\mathrm{\&tau;}}_i)}s(t-{\mathrm{\&tau;}}_i)\right]$

Wherein it is the time delay on the i-th paths.Make in formula be normalized calculating, then Received signal strength be expressed as orthogonal form,

$\tilde{r}\left(t\right)=u_1\left(t\right)\cos \left(t\right)\cos 2\mathrm{\&pi;}{f}_{c}t+u_2\left(t\right)j\sin 2\mathrm{\&pi;}{f}_{c}t$

Wherein

$u_1\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l}_i\cos 2\mathrm{\&pi;}{f}_c(t-{\mathrm{\&tau;}}_i)$

$u_2\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l}_i\sin 2\mathrm{\&pi;}{f}_c(t-{\mathrm{\&tau;}}_i)$

When N is very large, can by u ₁(t) and u ₂t () is considered as relatively independent Gaussian random process, and due to multipath delay random, can think phase angle 2 π f _c(t-τ _i) [-π, π) upper obedience is uniformly distributed, then according to auto-correlation function, can obtain Received signal strength envelope gross power

$P=\left(E\right[{u_1}^2\left(t\right)]+E[{u_2}^2\left(t\right)\left]\right)\&CenterDot;P_s=P_s\&CenterDot;\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{l_i}^2$

But because of multipath effect, cause phase difference between Different Diameter, and cause signal amplitude to offset, the multipath noise power P of the generation of multipath effect part _mfor

$P_m=\frac{P-P_b}{2}$

Wherein, P _bfor the large scale component power of the wireless signal that reception antenna receives;

Then considering that the actual signal to noise ratio of system under multipath effect is

$\mathrm{SNR}=\frac{P_b}{P_m+\mathrm{AWGN}}$

Wherein, AWGN is additive white Gaussian noise;

In a communications system, the pass of Packet Error Ratio and signal to noise ratio is:

$\mathrm{PER}=\frac{(M-1)}{2}\&times;e^{-\frac{\mathrm{SNR}}{2}}$

Wherein, M is the heterogeneous coefficient of modulation, and distinguishingly, under QPSK condition, M gets 4.

According to surveying the PER that draws, as shown in Figure 6, and adopt large-scale model to draw obtain described large scale component P _bpredicted value P _b', to PER and P _mcarry out curve fitting, progressively instead to push away, finally draw the multiple dimensioned channel fading model of reality considered under Small-scale fading.

$\mathrm{PL}=(a^{\&prime;}\&CenterDot;\ln \left(H_b\right)+b^{\&prime;}\&CenterDot;\ln \left(\mathrm{PSAD}\right)+c^{\&prime;})1\mathrm{gd}+1g(i\&CenterDot;e^{H_b}+j\&CenterDot;e^{\mathrm{PSAD}}+k)+B$

Wherein a ', b ', c ', i, j, k are fitting coefficient, and in Different growth phases, the value of PSAD is different, thus can draw different fitting coefficients, generally to corn growth process, can divide emerge, jointing, heading three phases carry out modeling analysis

$B=20\mathrm{lgf}-10\lg \left[\frac{G_s{G}_r{c}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\right]+\lg \frac{2}{3}$

It is a constant under established condition.Distinguishingly, the ln that in formula, matching uses, lg, e ^xbe not unique solution Deng function, according to embodiment of the present invention measured data, use the fitting degree of this function higher, and there is certain representativeness.

4. coefficient of determination fitting effect is evaluated

Coefficient R ²∈ [0,1] is mainly used to weigh the relation between model path loss measurement and predicted value, R ²more close to 1, then show path loss model estimated value and the higher degree of fitting of measured value correlation high, path loss fitting effect is good, the reaction actual communication situation that now model more can be definite, R ²computing formula is as follows.

$R^2=1-\frac{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\widehat{\mathrm{\&xi;}]}}^2}{\underset{i=1}{\overset{S}{\mathrm{\&Sigma;}}}{[{\mathrm{\&xi;}}_i-\stackrel{\&OverBar;}{\mathrm{\&xi;}}]}^2}$

Wherein, ξ _ithe radio signal propagation characteristic gathered, described ξ _iregressand value, for described ξ _imean value, S is the number of collecting sample point.According to the method that the present invention proposes, with 2.4G wireless signal for objective for implementation, carry out the modeling of channel fading characteristic, the R of matched curve ²be up to 0.997, minimum is 0.908, illustrates that the explanation degree of independent variable to dependent variable is high, has rebuild the radio sensor network channel feature of maize field environment preferably.

Although describe embodiments of the present invention by reference to the accompanying drawings, but those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, such amendment and modification all fall into by within claims limited range.

Claims (3)

1. the modeling method of the multiple dimensioned fading model of maize field radio sensor network channel, it is characterized in that, the method comprises:

S1. according to the envirment factor affecting radio signal propagation in sample area, the key factor causing channel space and time difference is extracted;

S2., under the varying environment factor and key factor condition, the radio signal propagation characteristic in sample area is gathered;

S3. according to the data gathered and described key factor, the multiple dimensioned fading model modeling of channel is carried out;

In step sl, described envirment factor comprises the height H of crop in sample area _p, crop leaf area A _l, crop stem stalk area A _c, crop fruit surface area A _f, land area A in sample area _g, distance d between transmitting antenna and reception antenna; What described key factor comprised crop blocks height H _b, crop surface area density indices P SAD, described H _b=H _a-H _p, wherein H _afor antenna height, the computing formula of described PSAD is as follows:

$PSAD=\frac{A_l+A_c+A_f}{A_G\&times;H_p};$

In step s 2, described sample area comprises H _bthe sample area of > 0 and H _bthe sample area of < 0, described radio signal propagation characteristic comprises H _bthe radio signal propagation characteristic of the sample area of > 0 and H _bthe radio signal propagation characteristic of the sample area of < 0;

Between step S2 and step S3, the method comprises further:

S21. at described H _bthe sample area of > 0, carries out channel large scale decline PL _bcalculating;

S22. described H is obtained _bthe radio signal propagation characteristic of the sample area of > 0, carries out matching to the data gathered, obtains channel large scale fading model;

In the step s 21, described channel large scale decline PL _bfor:

${\mathrm{PL}}_b=10\&CenterDot;lg\left(\frac{P_s}{P_r}\right)$

Wherein, P _sfor the transmitting power of transmitting antenna, P _rfor the power module of the wireless signal that reception antenna receives;

Described $P_r=\frac{P_s{G}_s{G}_r{\mathrm{\&lambda;}}^2}{{\left(4\mathrm{\&pi;}\right)}^2{d}^n};$

Wherein, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, λ is wavelength, and d is the distance between transmitting antenna and reception antenna, and n is fading factor;

Described step S22 comprises: obtain described H _bthe radio signal propagation characteristic of the sample area of > 0, by described radio signal propagation data to described P _rcarry out matching, obtain fading factor n and H _b, PSAD functional relation:

n＝a·ln(H _b)+b·ln(PSAD)+c

Described channel large scale fading model is:

PL＝(a·ln(H _b)+b·ln(PSAD)+c)lgd+A

Wherein, H _b> 0, PL is the decline of the wireless signal that transmitting antenna sends, and a, b and c are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines, H _bfor blocking height;

Wherein, $A=20\lg f-10\lg \&lsqb;\frac{G_s{G}_r^{2}c}{{\left(4\mathrm{\&pi;}\right)}^2}\&rsqb;,$ F is radio signal frequency, G _s, G _rbe respectively transmitting antenna and receiving antenna gain, c is speed of light constant;

Described step S3 comprises:

The envelope gross power P of the wireless signal S31. received according to reception antenna, described envelope gross power P comprises large scale component power P _band small scale multipath noise power P _m, build described P _mmodel:

$P_m=\frac{P-P_b}{2}$

Wherein, the formula of described P is as follows:

$P=P_s.\underset{i=1}{\overset{N}{\&Sigma;}}{l}_i^2$

Wherein, P _sfor the transmitting power of transmitting antenna, N is the number of multiple paths; l _iit is the reflection fading coefficients of the i-th paths;

S32. according to described P _m, build signal to noise ratio snr model;

$SNR=\frac{P_b}{P_m+AWGN};$

Wherein, AWGN is additive white Gaussian noise;

S33. according to described SNR, Packet Error Ratio PER model is built;

$PER=\frac{(M-1)}{2}\&times;e^{-\frac{SNR}{2}};$

Wherein, M is the heterogeneous coefficient of modulation;

S34. described H is obtained _bthe radio signal propagation characteristic of the sample area of < 0, obtains the Packet Error Ratio PER measured value of reception antenna;

According to H _benvironment key factor H in the sample area of < 0 _b, PSAD, and channel large scale fading model, obtains described large scale component power P _bpredicted value P _b';

S35. according to described PER measured value and described large scale component power P _bpredicted value P _b', to described PER model and P _mmodel carries out curve fitting, and obtains the multiple dimensioned fading model of channel:

$PL=(a^{\&prime;}\&CenterDot;ln(H_b)+b^{\&prime;}\&CenterDot;ln\left(PSAD\right)+c^{\&prime;})lgd+lg(i\&CenterDot;e^{H_b}+j\&CenterDot;e^{PSAD}+k)+B$

Wherein, H _b<0, PL are the decline of the wireless signal that transmitting antenna sends, and a ', b ', c ', i, j, k are fitting coefficient, and the PSAD that described fitting coefficient is corresponding according to crop growth stage determines;

Wherein, $B=20lgf-10lg\&lsqb;\frac{G_{sr}{\mathrm{GC}}^2}{{\left(4\mathrm{\&pi;}\right)}^2}\&rsqb;+lg\frac{2}{3},$ F is radio signal frequency, G _sfor transmitter antenna gain (dBi), c is speed of light constant.

2. method according to claim 1, is characterized in that, in step s 2, described radio signal propagation feature comprises: signal strength signal intensity, Packet Error Ratio.

3. method according to claim 1 and 2, is further characterized in that, described method comprises further: S4. is according to coefficient R ²evaluate the model obtained, wherein said R ²computing formula as follows:

$R^2=1-\frac{\underset{i=1}{\overset{S}{\&Sigma;}}{\&lsqb;{\mathrm{\&xi;}}_i-\widehat{\mathrm{\&xi;}}\&rsqb;}^2}{\underset{i=1}{\overset{S}{\&Sigma;}}{\&lsqb;{\mathrm{\&xi;}}_i-\stackrel{\&OverBar;}{\mathrm{\&xi;}}\&rsqb;}^2}$

Wherein, ξ _ithe radio signal propagation characteristic gathered, described ξ _iregressand value, for described ξ _imean value, S is the number of collecting sample point.